@inproceedings{sun-etal-2026-beyond,
title = "Beyond Sentence-level Labels: Integrating Conversational Context and Personal Experience for Natural Emotional Expression",
author = "Sun, Haiyang and
Le, Chenyang and
Wang, Wei and
Zhang, Leying and
Li, Chuang and
Han, Bing and
Li, Chenda and
Bi, Mengxiao and
Qian, Yanmin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.940/",
pages = "18839--18854",
ISBN = "979-8-89176-395-1",
abstract = "Emotional Text-to-Speech aims to synthesize speech with human-like naturalness and expressiveness. However, existing systems rely on sentence-level labels, which fails to capture the subtle nuances of human affect. Based on cognitive appraisal theories, we argue that emotional expression is not generated in isolation but is deeply influenced by speaker{'}s Personal Experience and the conversational Context.To overcome the information bottleneck inherent in traditional annotations, we present Emotional-Context-Speech, a large-scale, context-aware speech corpus derived from multi-speaker audiobooks. This dataset provides not only transcriptions but also dialogue context, personal experience, open-vocabulary emotion labels, and paralinguistic descriptions.Experimental results demonstrate that TTS model trained using additional context and experience descriptions as inputs, called Emotional-Context-TTS, significantly outperforms existing methods in terms of emotional expression accuracy and naturalness."
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<abstract>Emotional Text-to-Speech aims to synthesize speech with human-like naturalness and expressiveness. However, existing systems rely on sentence-level labels, which fails to capture the subtle nuances of human affect. Based on cognitive appraisal theories, we argue that emotional expression is not generated in isolation but is deeply influenced by speaker’s Personal Experience and the conversational Context.To overcome the information bottleneck inherent in traditional annotations, we present Emotional-Context-Speech, a large-scale, context-aware speech corpus derived from multi-speaker audiobooks. This dataset provides not only transcriptions but also dialogue context, personal experience, open-vocabulary emotion labels, and paralinguistic descriptions.Experimental results demonstrate that TTS model trained using additional context and experience descriptions as inputs, called Emotional-Context-TTS, significantly outperforms existing methods in terms of emotional expression accuracy and naturalness.</abstract>
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%0 Conference Proceedings
%T Beyond Sentence-level Labels: Integrating Conversational Context and Personal Experience for Natural Emotional Expression
%A Sun, Haiyang
%A Le, Chenyang
%A Wang, Wei
%A Zhang, Leying
%A Li, Chuang
%A Han, Bing
%A Li, Chenda
%A Bi, Mengxiao
%A Qian, Yanmin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F sun-etal-2026-beyond
%X Emotional Text-to-Speech aims to synthesize speech with human-like naturalness and expressiveness. However, existing systems rely on sentence-level labels, which fails to capture the subtle nuances of human affect. Based on cognitive appraisal theories, we argue that emotional expression is not generated in isolation but is deeply influenced by speaker’s Personal Experience and the conversational Context.To overcome the information bottleneck inherent in traditional annotations, we present Emotional-Context-Speech, a large-scale, context-aware speech corpus derived from multi-speaker audiobooks. This dataset provides not only transcriptions but also dialogue context, personal experience, open-vocabulary emotion labels, and paralinguistic descriptions.Experimental results demonstrate that TTS model trained using additional context and experience descriptions as inputs, called Emotional-Context-TTS, significantly outperforms existing methods in terms of emotional expression accuracy and naturalness.
%U https://aclanthology.org/2026.findings-acl.940/
%P 18839-18854
Markdown (Informal)
[Beyond Sentence-level Labels: Integrating Conversational Context and Personal Experience for Natural Emotional Expression](https://aclanthology.org/2026.findings-acl.940/) (Sun et al., Findings 2026)
ACL
- Haiyang Sun, Chenyang Le, Wei Wang, Leying Zhang, Chuang Li, Bing Han, Chenda Li, Mengxiao Bi, and Yanmin Qian. 2026. Beyond Sentence-level Labels: Integrating Conversational Context and Personal Experience for Natural Emotional Expression. In Findings of the Association for Computational Linguistics: ACL 2026, pages 18839–18854, San Diego, California, United States. Association for Computational Linguistics.